A class-modularity for character recognition

Abstract

A class-modular classifier can be characterized by two prominent features: low classifier complexity and independence of classes. While conventional character recognition systems adopting the class modularity are faithful to the first feature, they do not investigate the second one. Since a class can be handled independently of the other classes, the class-specific feature set and classifier architecture can be optimally designed for a specific class Here we propose a general framework for the class modularity that exploits fully both features and present four types of class-modular architecture. The neural network classifier is used for testing the framework A simultaneous selection of the feature set and network architecture is performed by the genetic algorithm. The effectiveness of the class-specific features and classifier architectures is confirmed by experimental results on the recognition of handwritten numerals.